Kavli Affiliate: Cheng Peng
| First 5 Authors: Cheng Peng, Cheng Peng, , ,
| Summary:
Natural language processing (NLP) is a key technology to extract important
patient information from clinical narratives to support healthcare
applications. The rapid development of large language models (LLMs) has
revolutionized many NLP tasks in the clinical domain, yet their optimal use in
patient information extraction tasks requires further exploration. This study
examines LLMs’ effectiveness in patient information extraction, focusing on LLM
architectures, fine-tuning strategies, and multi-task instruction tuning
techniques for developing robust and generalizable patient information
extraction systems. This study aims to explore key concepts of using LLMs for
clinical concept and relation extraction tasks, including: (1) encoder-only or
decoder-only LLMs, (2) prompt-based parameter-efficient fine-tuning (PEFT)
algorithms, and (3) multi-task instruction tuning on few-shot learning
performance. We benchmarked a suite of LLMs, including encoder-based LLMs
(BERT, GatorTron) and decoder-based LLMs (GatorTronGPT, Llama 3.1,
GatorTronLlama), across five datasets. We compared traditional full-size
fine-tuning and prompt-based PEFT. We explored a multi-task instruction tuning
framework that combines both tasks across four datasets to evaluate the
zero-shot and few-shot learning performance using the leave-one-dataset-out
strategy.
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